This study shows app usage is relatively low among people with diabetes, while 60.2% of HPs have recommended an app to patients. There is, however, interest amongst people with diabetes and HPs to use diabetes apps, with strong interest in an insulin dose calculator. Apps with this feature have the potential to improve diabetes control. However, the critical problem of app safety remains a barrier to the prescription and use of insulin dose calculators. Further work is needed to ensure apps are safe and provided in a regulated environment. An app assessment process would provide HPs with confidence in the apps they recommend and would ultimately ensure app quality and safety for app users. At present, however, app users and HPs must remain cautious with diabetes apps, especially those in the insulin dose calculator category.

This patient sample came from patients in secondary care diabetes clinics, and therefore, app use may be different amongst patients managed in primary care. Similarly, findings may not generalize to patients with poorer glycemic control as responders had statistically significantly lower HbA1c than non-responders. This was a cross-sectional survey that is useful to assess app use at one point in time, but it is likely that people vary their app use and recommendations over time. It was therefore not possible to assess whether the introduction of an app has significant effect on clinical outcomes. Our study did not address the difference in needs in app features between responders on insulin and those not on insulin. Overall the response rates for both surveys were low and responses were limited by self-report and therefore liable to responder bias.
Owing to time restrictions, longer term follow-up of participants was not feasible within the current study, although it is hoped that a two year follow-up of the present study’s participants is possible. The significant group difference seen at three months, dropping slightly at six months, but reaching significance again at nine months, could be an indication of sustained change. Another limitation of the study design was that secondary outcome assessors were not blinded to treatment allocation, which could have introduced bias in follow-up data collection of secondary variables.
The {Dario} device has been perfect, I love it. I love that it’s small and discreet enough. I can now test my sugars within 20 seconds, all from the bottom of my iPhone and no one around is none the wiser… I also love that it’s “all in one”. I’ve been using it now for around 4 – 5 months. The app is great at logging and motivation with its % scoring system.
The good news is that there are things you can do to prevent these diabetes-related problems, no matter your age. Taking action now will help with your later years, so you can live a healthy life and see your grandchildren grow into beautiful and healthy men and women. And, it’s the perfect time to think about this because National Grandparents Day is on Sunday.
However, there are concerns about the appropriateness and safety of apps for diabetes self-management [5,11-13,15]. In 2013 only 1 of 600 diabetes apps reviewed in the USA had received FDA clearance [11]. Similarly a review, specifically of insulin dose calculator apps, determined that only one of 46 calculators was clinically safe. The most common issue was that calculators accepted implausible values for blood glucose readings (eg, negative values), yet would still provide an advised insulin dose [15]. HPs are also concerned about app safety [19] and are advised to take care when advising apps to patients [15]. In the United Kingdom, The Royal College of Physicians Health Informatics Unit (London) has developed a checklist for assessing app quality [19]. However, the multitude of factors HPs must consider while recommending apps, including patient familiarity with technology, app features, ease of use, and FDA approval [19] may be burdensome and not practical in day to day clinical care.
-Learn to eat well-balanced meals that include healthful food choices (vegetables, fruits, whole grains, etc.) and watch your portion sizes. Even foods that are good for you can add pounds to your waistline, if you consume too much of them. Losing those extra pounds will help you manage not only your diabetes, but also other health problems you may have.
Similar to a national American mHealth survey, a large proportion of patients are not using health apps [26]. However, there was a higher rate (20%) of diabetes app use in this patient group compared to the 4% found in a survey of diabetes app use in the USA in 2015 [14] and 7% in Scotland in 2016 [23]. Our findings are consistent with previous surveys showing people using apps are more likely to be younger [26]. It has been suggested that people who are more in need of diabetes care are less likely to use apps [27]; however, we found no significant difference in HbA1c between app users and non-app users. The most favored feature being the blood glucose diary is not surprising given it is the most common feature included in the apps available [5,14]. However some responders are also using health apps that are not specific to diabetes, such as apps for dietary advice.

There were 884 new cases of type 1 diabetes, and age at diagnosis rose from 7.6 yr in 1990/1 to 8.9 yr in 2008/9 (r2 = 0.31, p = 0.009). There was a progressive increase in type 1 diabetes incidence among children <15 yr (p<0.0001), reaching 22.5 per 100,000 in 2009. However, the rise in incidence did not occur evenly among age groups, being 2.5-fold higher in older children (10–14 yr) than in the youngest group (0–4 yr). The incidence of new cases of type 1 diabetes was highest in New Zealand Europeans throughout the study period in all age groups (p<0.0001), but the rate of increase was similar in New Zealand Europeans and Non-Europeans. Type 1 diabetes incidence and average annual increase were similar in both sexes. There was no change in BMI SDS shortly after diagnosis, and no association between BMI SDS and age at diagnosis.
‘I was very pleased to contact your service. I was feeling overwhelmed with my current situation however knew that I needed to get a diabetes test done. While I was waiting for my turn to be tested Susan welcomed me, helped my overwhelming feelings calm down, she was very approachable and understanding. Sandy followed through by assisting me with assurance that things were going to be okay and was very understanding. She encouraged that I seek more medical advice for my blood pressure results. She phoned my manager and found me a local GP that I could visit right away. I was very appreciative of these ladies and all the help, care and advice they gave me. Thank you so much!’
For example, adjusting to having diabetes; difficulty in making the life changes necessary to stay well; difficulty managing anger, conflict and other emotions related to your health; depression, sadness and grief; anxiety, worries, panic and phobias related to your health; eating difficulties; and difficulty with coping with the complications of diabetes.
Data were imported into SPSS version 24 (IBM). Incomplete responses were included in the analysis. In the patient survey, independent sample t tests were conducted to compare mean clinical variables (age, BP, C:HDL, LDL, HbA1c) by type of diabetes, method of recruitment, and whether the responder used a diabetes mobile phone app. Adjustment was made for unequal variances. Normal distribution was assumed for all variables, apart from urinary microalbumin to creatinine for which a Wilcoxin test was used. No statistically significant differences in these variables or in mobile phone app use were found between patients with recorded email addresses and patients phoned for their email address. Therefore, all 189 responses were combined for further analysis. Chi-square tests were used to compare medications and survey responses by type of diabetes. Statistical significance was determined by exact 2-sided P values less than .05. In the HP survey, mean values on the usefulness and confidence Likert scales were calculated to compare app features.
This study shows that the incidence of type 1 diabetes in the Auckland region has increased steadily over the last two decades. However, unlike other studies [3], [4], [5], the rate of increase in incidence has been particularly marked in older children (10–14 yr), which was approximately 2.5-fold greater than that in children 0–4 yr. Interestingly, the incidence of type 1 diabetes in children 0–4 and 10–14 in Auckland are very similar to those reported in Australia, our closest geographical and ethnic neighbours [19], both of which had very high case ascertainment levels (close to 100%).

Height and weight were recorded for 660 patients at their required first post-diagnostic clinic (on average 15 weeks from diagnosis) from 1994 onwards. Annual mean BMI SDS of newly diagnosed type 1 diabetes did not alter (average non-significant change smaller than ±0.02 SDS/year) over the period for the entire population, or for any gender, age, or ethnicity sub-group. There was no association between BMI SDS and age at diagnosis.


However, there are concerns about the appropriateness and safety of apps for diabetes self-management [5,11-13,15]. In 2013 only 1 of 600 diabetes apps reviewed in the USA had received FDA clearance [11]. Similarly a review, specifically of insulin dose calculator apps, determined that only one of 46 calculators was clinically safe. The most common issue was that calculators accepted implausible values for blood glucose readings (eg, negative values), yet would still provide an advised insulin dose [15]. HPs are also concerned about app safety [19] and are advised to take care when advising apps to patients [15]. In the United Kingdom, The Royal College of Physicians Health Informatics Unit (London) has developed a checklist for assessing app quality [19]. However, the multitude of factors HPs must consider while recommending apps, including patient familiarity with technology, app features, ease of use, and FDA approval [19] may be burdensome and not practical in day to day clinical care.
Participants could choose to receive blood glucose monitoring reminders to which they could reply by sending in their result by text message. They could then view their results graphically over time on a password protected website. If they were identified as not having access to the internet at baseline they were mailed their graphs once a month. All messages were delivered in English although the Māori version included keywords in Te Reo Māori and the Pacific version had keywords in either Samoan or Tongan dependent on ethnicity. Examples of SMS4BG messages can be seen in the box. Participants were able to select the timing of messages and reminders, and identify the names of their support people and motivations for incorporation into the messages. The duration of the programme was also tailored to individual preferences. At three and six months, participants received a message asking if they would like to continue the programme for an additional three months, and had the opportunity to reselect their modules receiving up to a maximum nine months of messages. Participants could stop their messages by texting the word “STOP” or put messages on hold by texting “HOLIDAY.”
We saw no significant interaction between the treatment group and any of the prespecified subgroups: type 1 versus type 2 diabetes (P=0.82), non-Māori/non-Pacific versus Māori/Pacific ethnicity (P=0.60), high urban versus high rural/remote region (P=0.38). Adjusted mean differences on change in HbA1c from baseline to nine months for patients with type 1 and type 2 diabetes were −5.75 mmol/mol (95% confidence interval −10.08 to −1.43, P=0.009) and −3.64 mmol/mol (−7.72 to 0.44, P=0.08), respectively. Adjusted mean differences for non-Māori/non-Pacific and Māori/Pacific people were −4.97 mmol/mol (−8.51 to −1.43, P=0.006) and −3.21 mmol/mol (−9.11 to 2.70, P=0.28), respectively. Adjusted mean differences for participants living in high urban and high rural/remote areas were −4.54 mmol/mol (−8.40 to −0.68, P=0.02) and −3.94 mmol/mol (−9.00 to 1.12, P=0.13), respectively (table 3).
We summarised the primary and secondary outcomes using descriptive statistics at each scheduled visit. A random effects mixed model was used to evaluate the effect of intervention on HbA1c at three, six, and nine months’ follow-up, adjusting for baseline HbA1c and stratification factors and accounting for repeated measures over time. Model adjusted mean differences in HbA1c between the two groups were estimated at each visit, by including an interaction term between treatment and month. Missing data on the primary outcome were taken into account in modelling based on the missing at random assumption. Both 95% confidence intervals and P values were reported. Treatment effects sizes were also compared between important subgroups considered in stratification, including diabetes type (1 and 2), ethnicity (Māori/Pacific and non-Māori/non-Pacific), and region (urban and rural). For other secondary outcomes measured at nine months, we used generalised linear regression models with same covariate adjustment using a link function appropriate to the distribution of outcomes. Model adjusted estimates on the treatment difference between the two groups at nine months were reported, together with 95% confidence intervals and P values. No imputation was considered on secondary outcomes.

A total of 793 individuals were referred to the study and assessed for eligibility between June 2015 and November 2016. Of these, 366 were randomised to the intervention and control groups (n=183 each; fig 1). The final nine month follow-up assessments were completed in August 2017, with loss to follow-up (that is, no follow-up data on any outcome) low in both groups (overall 7/366=2%). A total of 12 participants (six per group) were excluded from the primary outcome analysis because of no follow-up HbA1c results after randomisation. Baseline characteristics of participants are presented in table 1, and no adverse events were recorded from the study or protocol deviations.


In this large sample of people with diabetes attending a secondary care clinic in NZ, 19.6% (37/189) of patients reported using diabetes apps to support their self-management. Diabetes app users were younger and more often had T1DM. The most used app feature in current app users was a blood glucose diary (87%, 32/37). The most desirable feature of a future app was an insulin dose calculation function in app users (46%) and a blood glucose diary in non-app users (64.4%). A Scottish survey has reported similar results and observed that people with T1DM were more likely to desire insulin calculators in an app [23].
Before my type1 insulin dependent diagnosis, I had a pancreas that worked, going out for dinner was ...really exciting. I didn’t even know what type one autoimmune disease was. Id pick whatever I wanted from the menu. Didn’t think of my blood sugars at all! Sitting at the table and I would drink my drink without a thought of what it will be doing when the drink rushes into my blood stream. I wouldn’t be calculating in my head if carbs totals and portion sizes are going to bring me into hyper or hypoglycaemia . I wouldn’t be hoping that the exercise id just done before going to the restaurant will change my blood glucose reading....Now....my pancreas hasn’t worked for 11years and while everyone’s chatting away at the table I’m half there in mind and half of me is not living in the moment of enjoying myself because I’m caught up in the complete intensity of trying to deal with my type one condition. Very overwhelming and my mind plays a 🤹‍♂️ juggling game where One ball is exercise, one ball is long and Quick acting insulin and one ball is carbs/food portion. Also, my will power either is good or it’s shocking. The others get their big portions while I’m still at bg testing stage and haven’t injected for the meal yet!! Everyone is trying each others food next to me and across the table. I have invisible blinkers on my eyes so I’m not aware of food sharing that’s going on. Once my food arrived it’s then that I can calculate how many units of my insulin that I inject depending on how many carbohydrates in the meal , making sure I inject in a different area to my lunchtime injection. Finally I begin to eat and the other people are almost finished their meal!!! I am a type one hero in more ways than one. See More

“There have been so many touching moments in the movement to Stop Diabetes since we launched last year,” commented Larry Hausner, CEO, American Diabetes Association. “People have shared courageous stories of facing their diabetes head on, while others have shared their heart-breaking experiences of losing a loved one because of diabetes. The blog is a new way to raise our collective voices and tell people why we need to Stop Diabetes once and for all.”  
There was a steady increase in the annual number of newly diagnosed cases of type 1 diabetes in children <15 yr (r2 = 0.80; p<0.0001) of 2.0 additional cases per year, from 23 in 1990/1 to 60 cases per year in 2008/9. There was no appreciable difference in the rate of increase between males and females (p = 0.08), but the rise in number of new type 1 diabetes cases did not occur evenly among age groups (p = 0.0001). The yearly increase among older children (10–14 yr) was 3-fold greater than in the youngest (0–4 yr) group (0–4 yr = +0.4/yr; 5–9 yr = +0.8/yr; 10–14 yr = +1.2/yr). Over the 20-year period, new cases were moderately more frequent in winter and less frequent in spring (29.4% and 22.0%, respectively; test of equal proportions across all four seasons: p = 0.02).
The average reduction of 4.2 mmol/mol (0.4%) in HbA1c seen in this study did not reach the level chosen to signify clinical significance in the initial power calculation (5.5 mmol/mol (0.5%) reduction in HbA1c). Therefore, this study is unable to conclude that the effects of the SMS4BG intervention are clinically significant. Although further investigation is needed, we believe the results have the potential to still be clinically relevant in practice, particularly among individuals with high levels of HbA1c, such as the participants with poorly controlled diabetes in this study. The unadjusted group difference on change in HbA1c from baseline was −5.89, −3.05 and −5.24 mmol/mol at three, six, and nine months, respectively. The main analysis, with adjustment for baseline value and stratification factors, showed a smaller treatment effect, although both results were significant at three and nine months. Similar results were found across major subgroups of interest despite the fact that these analyses were not specifically powered. These consistent findings led us to believe that the intervention shows promising effects on treating people with poorly controlled diabetes and warrants further investigation.
The growing prevalence of diabetes is considered to be one of the biggest global health issues.1 People of ethnic minorities, including Pacific and Māori (New Zealand indigenous population) groups, are particularly vulnerable to the development of diabetes, experience poorer control, and increased rates of complications.23456 In New Zealand, 29% of patients with diabetes were found to have HbA1c levels indicative of poor control (≥65 mmol/mol or 8%), putting them at risk for the development of debilitating and costly complications.7 Diabetes complications can be prevented or delayed with good blood glucose control, which is not only advantageous for a person’s quality of life but also will substantially reduce healthcare costs associated with treating or managing the complications.89101112

The 1177 people with diabetes attending clinics at Capital and Coast District Health Board (CCDHB), Wellington, New Zealand over a 12-month period (10th September 2014 to 10th September 2015) were the sample population. Out of the total patients, 521 patients with an email address in the hospital management system were invited to participate via email. To include a representation of people without a recorded email address in the sample (n=656), every 5th person was telephoned (up to twice) and invited to provide an email address. Of the 131 patients telephoned, 54 (41.2%) were reached, of whom 49 (91%) agreed to participate. Patients without phone numbers or unable to provide an email address were excluded. This generated a sample population of 570 people.
Before my type1 insulin dependent diagnosis, I had a pancreas that worked, going out for dinner was ...really exciting. I didn’t even know what type one autoimmune disease was. Id pick whatever I wanted from the menu. Didn’t think of my blood sugars at all! Sitting at the table and I would drink my drink without a thought of what it will be doing when the drink rushes into my blood stream. I wouldn’t be calculating in my head if carbs totals and portion sizes are going to bring me into hyper or hypoglycaemia . I wouldn’t be hoping that the exercise id just done before going to the restaurant will change my blood glucose reading....Now....my pancreas hasn’t worked for 11years and while everyone’s chatting away at the table I’m half there in mind and half of me is not living in the moment of enjoying myself because I’m caught up in the complete intensity of trying to deal with my type one condition. Very overwhelming and my mind plays a 🤹‍♂️ juggling game where One ball is exercise, one ball is long and Quick acting insulin and one ball is carbs/food portion. Also, my will power either is good or it’s shocking. The others get their big portions while I’m still at bg testing stage and haven’t injected for the meal yet!! Everyone is trying each others food next to me and across the table. I have invisible blinkers on my eyes so I’m not aware of food sharing that’s going on. Once my food arrived it’s then that I can calculate how many units of my insulin that I inject depending on how many carbohydrates in the meal , making sure I inject in a different area to my lunchtime injection. Finally I begin to eat and the other people are almost finished their meal!!! I am a type one hero in more ways than one. See More
Strengths of the intervention were that it was theoretically based, the information reinforced messages from standard care, and it was system initiated, personally tailored, and used simple technology. These strengths result in high relevance to diverse individuals, increasing the intervention’s reach and acceptability. Unlike SMS4BG, previous diabetes SMS programmes have largely focused on specific groups—for example, limiting their generalisability. Furthermore, the SMS4BG intervention was tailored and personalised to the individual. Although this specificity results in a more complex intervention in relation to its delivery, it appears to be a worthwhile endeavour with high satisfaction and the majority of participants happy with their message dosage.
The Endocrinology Service at Starship Children's Health provides specialist care for all children diagnosed with type 1 diabetes in the Auckland region (New Zealand). Its Paediatric Diabetes Service provides centralised medical care for all diabetic children up to 15 yr who reside in the Auckland region, drawing from the regional population of approximately 1.5 million [12]. All children or adolescents diagnosed with type 1 diabetes who attended the Paediatric Service between 1 January 1990 and 31 December 2009 were eligible for this study. Subjects were captured from a comprehensive database (Starbase) that gathers data on all children with type 1 diabetes in the Auckland region. This information was cross-referenced with hospital admission data and subsequent clinical follow up, leading to a case ascertainment >95% for children with type 1 diabetes [13].
×